Algorithm REF  is a technique to learn policies for an RL-agent with a CPT-based objective in a privacy-preserving manner.
The target privacy level \((\epsilon , \delta )\)  determines the noise level that will be needed to ensure differential privacy (Line 2).
The linked-list \(PrivPres\)  maintains a record of states visited along sample trajectories in the replay buffer, since parameters of the Gaussian noise added to the value functions at each step will depend on the `chain' of states in this list (Line 7).
The training process consists of working with batches of sample trajectories that have been collected in a replay buffer.
A quadratic loss function that measures the TD-error with respect to a target \(Q-\) network (whose parameters are kept fixed) is minimized in Line 10.
The parameters of the target network are updated after examining all the samples from a batch using a gradient-based method (Line 15) [1]}.
Lines 10 and 15 together comprise the TD-update.
